Course Goals

1. Help you get started writing your second year paper and jobmarket paper.

2. Introduce you to macro literatures with a strong empiricalcomponent and the datasets used in these literatures.

Towards those goals:

I Problem sets

I Final Paper/Presentation

Notes on Heathcote et al. (2010):"Unequal we stand: An empiricalanalysis of economic inequality inthe United States, 1967-2006"

Research Questions

I How has cross-sectional inequality changed in the US over thelast 4+ decades?

I How does our understanding of inequality depend on...I the income/consumption measure?I the measure of inequality (e.g. variance of log, ginicoeffi cient)?

I the data source?

Why are these questions important?

I Differences between income and consumption inequality areinformative about interesting objects:

I duration/persistence of random income shocksI effectiveness of insurance and public policy mechanismsavailable to households

I Many datasets, each with their own strengths and weaknesses.I Important to know whether the income measures line up.

Current Population Survey (CPS)

I Approx. 150K individuals per year.I Monthly Sample

I Individuals surveyed for 4 months, then 4 months a year later.I Employment, education, demographic and geographicvariables.

I 1976 to present.

I March SampleI Richer data on sources of income, work.I 1962 to present.

I Disadvantages: Weak panel dimension. Little info onconsumption.

Panel Survey of Income Dynamics (PSID)

I Approx. 5-10K individuals, 1968 to the present.I Annual up to 1996; bi-annual beginning in 1999.

I Main advantages:I Can track individuals/families over time.I Income, asset holding, and demographic data.

I Disadvantages:I Not nationally representative (oversamples whites).I Little info on consumption, especially early on in the sample

I Food and housing since ’68I Education and health care since ’99I Furnishing, clothing, recreation, transportation since ’05

Consumption Expenditure Survey (CEX)

I Approx. 5K individuals.I Two types: Weekly Diary Survey and Interview.I Rich data on expenditures on different (approx. 700)categories of goods and services.

I Some data on sources of income, education, demographics.I How to access:

I 1980-present: ICPSRI 1996-present: BLS Website:https://www.bls.gov/cex/pumd.htm

I Disadvantages: Much less geographic info. Missing a large,growing fraction of consumption expenditures.

Survey of Consumer Finances (SCF)

I Approx. 3-7K individuals; rich are oversampledI 1980s to the present, every 3 yearsI Rich data on labor income, loans, asset holdings, income fromassets.

I Limited panel dimension (short panels in 1983-89 and2007-09)

Basis of ComparisonHow well do survey aggregates match up to those in the NIPA data?

I National Income and Product Accounts (NIPA) are 7 sets oftables on

I GDP and its componentsI personal incomeI government income and expendituresI foreign transactionsI saving and investmentI (labor and capital) income by industry.I etc...

I Many data sources: Census, BLS, IRS, Treasury Department,Dept. of Agriculture, Offi ce of Management and Budget.

I Double entry; Adjustments seek consistency across tables.I Only data on aggregates.

CPS and NIPA match up for labor income, not for pre-taxincome

I CPS "misses" in-kind compensation (e.g., employercontributions to pension and health insurance funds).

Discrepancy between aggregate CEX consumption andNIPA consumption is big, increasing.

The household budget constraint

c +(a′ − a

)= wm lm + wm lw + yAsset + tPrivate + tGovt.

I Several determinants of household consumption inequality:I individual labor supplyI labor income pooling within the familyI income from asset ownershipI private transfersI government taxes and transfers

I The shares of income from these different income sources, andthe correlations across income sources, shape consumptioninequality.

Inequality in hourly wages is increasing.

c +(a′ − a

)= wm lm + ww lw + yAsset + tPrivate + tGovt.

I Gini coeffi cient tracks 90-50 ratio; Variation of Log Incometracks 50-10 ratio.

2/3 of the increase is from "residual" income inequality.

c +(a′ − a

)= wm lm + ww lw + yAsset + tPrivate + tGovt.

Inequality in labor earnings is increasing for men.

c +(a′ − a

)= wm lm + ww lw + yAsset + tPrivate + tGovt.

Inequality in household labor earnings is increasing.

c +(a′ − a

)= wm lm+ww lw + yAsset + tPrivate + tGovt.

Inequality in household labor earnings is increasing.

c +(a′ − a

)= wm lm+ww lw + yAsset + tPrivate + tGovt.

Inequality, when including asset income and privatetransfers, is lower

c +(a′ − a

)= wm lm+ ww lw+ yAsset+tPrivate+tGovt.

Inequality, when including taxes and government transfers,is even lower

c +(a′ − a

)= wm lm+ww lw+yAsset+tPrivate+tGovt.

Inequality in wealth is increasing

c +(a′ − a

)= wm lm + wm lw + yAsset + tPrivate + tGovt.

CEX: Inequality in expenditures is relatively flat.

c +(a′ − a

)= wm lm+ww lw+yAsset+tPrivate+tGovt.

CEX: Between/within group changes in inequality

c +(a′ − a

)= wm lm+ww lw+yAsset+tPrivate+tGovt.

Residual Variance Between-Group Variance

­.05

0.0

5.1

.15

Cha

nge 

in v

aria

nce 

of lo

g co

nsum

ptio

n (o

r inc

ome)

1980 1985 1990 1995 2000 2005Y ear

0.0

2.0

4.0

6.0

8C

hang

e in

 var

ianc

e of

 log 

cons

umpt

ion 

(or i

ncom

e)

1980 1985 1990 1995 2000 2005Y ear

I Income inequality growth is largely within group.I Consumption inequality growth is largely between group.I Krueger and Perri (2006): These patterns are indicative ofeffective within-group insurance.

SummaryI Inequality is increasing

I First half of the sample: both 50-10 inequality and 90-50inequality

I Second half of the sample: 90-50 inequality only

I According to the CEX, expenditure inequality increases only alittle.

I Trends in earnings inequality are similar in the four datasetswe looked at.

I Micro data aggregates (increasingly) miss some componentsof income and expenditures.

I Also part of the same issue of the Review of EconomicDynamics: Analysis of inequality in Canada, GB, Germany,Italy, Spain, Sweden, Russia, Mexico.

Mexican/Canadian consumption inequality is alsoincreasing.

Aguiar and BilsHas Consumption Inequality Mirrored Income Inequality?

.2.4

.6.8

1

Rat

io o

f Hou

seho

ld E

xpen

ditu

res,

CEX

 vs. N

IPA

1980 1990 2000 2010Year

Aguiar and BilsHas Consumption Inequality Mirrored Income Inequality?

Health care

Serv ices

Goods.2

.4.6

.81

Rat

io o

f Hou

seho

ld E

xpen

ditu

res,

CEX

 vs. N

IPA

1980 1990 2000 2010Year

Aguiar and BilsHas Consumption Inequality Mirrored Income Inequality?

I Hypothesis: Measurement error accounts for the "missing"increase in consumption inequality.

I Goal: Estimate "true" expenditures at time t for householdswith income i

I Basic idea: Compare expenditures on income-elastic goods(entertainment, cash donations) to expenditures on inelasticgoods (food at home, utilities) for groups of different incomeclasses over time.

I In the CEX, inequality in entertainment expenditures increasesemuch faster than income on food at home expenditures.

I Main result: Consumption inequality tracks income inequality.

Income elasticities: β

Tobac

coFoo

d at h

ome

UtilitiesChil

dren's

 cloth

ing

Applia

nces

All othe

r tran

sport

Health

 care

Housin

g

Person

al ca

re

Vehicl

es

ShoesAlco

hol

Enterta

inmen

t equ

ipmen

t

Food o

ut

Adult c

lothin

g

Furnitu

reDom

estic

 servi

ces

Educa

tion

Enterta

inmen

t fees

Cash c

ontrib

ution

s

­.50

.51

1.5

22.

5β

Two main assumptions

1. Log-linear Engel curves:

log x∗hjt − log x̄∗jt = α∗jt + βj logX ∗ht + Γj Zh︸︷︷︸hh characteristics

+ ϕhjt︸︷︷︸taste shock

I Zh = number of earners (<2, 2+); household size; age (25-37,38-50, 51-64)

2. Household expenditures measurement error takes threecomponents:

xhjt = x∗hjteζhjt , where

ζhjt = ψjt + φit + υhjt

I ψjt : good-specific measurement errorI φit : income-group-specific measurement error.I Main assumption: υhjt , ϕhjt are orthogonal to householdcharacteristics or βj .

Growth of expenditures for high and low income groups

log xijt = αjt + φit + logX ∗itβj + εijt

clothes

alcbev

cashcont

chldrnclothes

educequpmt

foodawayfoodhome

ent

furniture

healthsvcs

trans

perscarehousing

shoes

tobacco

tvutilities auto

­.25

0.2

5.5

.75

1Ex

pend

iture

 Gro

wth

0 .5 1 1.5 21994­96 E lasticities

clothes

alcbev

cashcont

chldrnclothes

educ

equpmtfoodawayfoodhome

ent

furniturehealth

svcs

trans

perscarehousing

shoes

tobacco

tvutilitiesauto

­.5­.2

50

.25

.5.7

51

Expe

nditu

re G

row

th

0 .5 1 1.5 21994­96 E lasticities

I Left: log(xPoor,j ,2007xPoor,j ,1980

)= ∆αj ,2007−1980 + log

(X ∗Poor,2007X ∗Poor,1980

)βj

I Right: log(xRich,j ,2007xRich,j ,1980

)= ∆αj ,2007−1980 + log

(X ∗Rich,2007X ∗Rich,1980

)βj

I Slopes = −0.15, 0.28 ⇒ Expenditure inequality increases by43 log points.

Notes on Aguiar and Hurst (2007):"Measuring Trends in

Leisure: The Allocation ofTime over Five Decades"

The lecture so far

I Heathcote et al. (2010)I Household earnings inequality has been increasing since the1970s.

I Most of the increase is in residual ("within group") inequality.I Consumption inequality is basically flat. The small increase ismostly between-group inequality.

I Aguiar and Bils (2013)I Consumption inequality actually increases at a rate similar tothat of income inequality.

We care about utility from consumption expenditures......not consumption expenditures per se.

I We defined consumption ≡ f (x1, ..., xn) as a function ofexpenditures.

I Relevant budget constraint:∑i

pi · xi︸ ︷︷ ︸expenditures on good i

= W · tW︸ ︷︷ ︸labor income

+ V︸︷︷︸other income

I Becker (1965): Consumption consists of a bundle ofcommodities c1, ..., ci , ..., cn

I Commodities are a combination of market goods (xi ) and timeinputs (ti ): ci = φi (xi , ti )

I Extra budget constraint:∑i

ti︸︷︷︸time spent on commodity i

= T − tW

Research question and method

I Data on the evolution of tW have been readily available (inthe PSID, CPS, NLSY, etc...) for awhile. Not so for thecomponents of T − tW .

I How have the components of T − tW (time spent not workingin the market) changed over time

I ... on average?I ... for men vs. women?I ... for individuals in different income groups?

I Method: Combine time-use surveys from 1965 to 2003 (someresults extended to 2013).

Data Sources

I Use only retrospective diaries. Individuals badly estimate timeuse without time diaries.

I Robinson and Godbey (1997): Someone with a diary showing38 (55) hours/wk reports, in a retrospective interview, working40 (70+) hours/wk

I Americans Use of Time (1965-1966), Time Use in Economicand Social Accounts (1975-1976), Americans’Use of Time(1985), National Human Activity Pattern Survey (1992-1994).

I 2K-9K individuals per dataset.

I American Time Use SurveyI Annual, beginning in 2003.I 20K in 2003, somewhat fewer in other yearsI Can be linked to the CPS.

Main results and their implications

Two main findings:

1. Average time spent on leisure has gone up, by roughly 4 to 8hours

2. Dispersion in leisure time also increasing

2.1 90-10 difference in leisure time increases by 14 hours2.2 Less educated increase their leisure time more.

Implications:

I GDP growth may understate welfare growthI Looking at consumption expenditures may overstate thegrowth of inequality in the past few decades.

Demographic Change

>=College,60­65

<HS,21­29

0.0

1.0

2.0

3S

hare

 in S

ampl

e

1970 1980 1990 2000 2010Year

I Most calculations "fix" demographic weights when computingaverages.

Time Categories

1. Market workI "Core": Main and second jobs, telecommuting workI "Total": Core + Commuting + Lunch Breaks at Work.

2. Non market workI Meal preparation, house cleaning, laundryI Shopping: obtaining gods and servicesI Home and vehicle maintenance, pet care.

3. Time with children

4. LeisureI Leisure 1: Entertainment, social and recreational activities,relaxing, gardening

I Leisure 2: "1" + Eating, sleeping, personal careI Leisure 3: "2" + child careI Leisure 4: "3" + civic activities, caring for other adults,education, medical care

What activities are leisure?

I Robinson and Godbey: activities that have high enjoymentI 1985 Time Use Survey rate activities from 0 to 10

Activity Index Activity IndexSex 9.3 Market work 7.0Play sports 9.2 Help adults 6.4Play with kids 8.8 Child care 6.4Talk/read to kids 8.6 Commute 6.3Church 8.5 Pet care 6.0Sleep 8.5 Homework 5.3TV 7.8 Yardwork 5.0Baby care 7.2 Child health 4.7Gardening 7.1 Car repair shop 4.6

I Margaret Reid (1934): Home production is time spent inactivities for which a market substitute could potentially exist.

I gaden + pet care, child care, care of others

Time Spent in Market Work

2030

4050

Hou

rs

1970 1980 1990 2000 2010Year

I Core work declines 8 hours for men, up 3 hours for womenI "Non-core" market work declines 6 hours for men, 2 forwomen.

Time Spent in Home Production

1015

2025

3035

Hou

rs

1970 1980 1990 2000 2010Year

I Declines 11 hours for women, up 3 hours for men.

Time Spent with Children

02

46

8H

ours

1970 1980 1990 2000 2010Year

I Increases 2 hours for both men and women.

Leisure Time

3032

3436

38H

ours

1970 1980 1990 2000 2010Y ear

102

104

106

108

110

112

Hou

rs

1970 1980 1990 2000 2010Y ear

I "Leisure 2 measure" increases roughly by 6 hours for men, 5for women.

Leisure Time: Changing Demographic Weights

3032

3436

38H

ours

1970 1980 1990 2000 2010Y ear

102

104

106

108

110

112

Hou

rs

1970 1980 1990 2000 2010Y ear

I Slightly larger increase in leisure-2 time, with changingdemographic weights.

Leisure: Sleep has increased by 7 hours/wk

5456

5860

62H

ours

1970 1980 1990 2000 2010Year

Leisure: TV has increased by 9 hours/wk

TV

PersonalCare

Socializing

Non­TVEntertainment

Eating

05

1015

20H

ours

1970 1980 1990 2000 2010Year

Leisure: Reading has decreased by 4 hours/wk

Hobbies

Reading

Exercise

Gardenand Pet

All Other0

12

34

5H

ours

1970 1980 1990 2000 2010Year

Distribution of leisure time

Percentile 1965 1975 1985 1993 2003 201310 74.7 77.4 77.0 75.5 72.9 74.125 85.2 88.1 88.4 87.5 85.8 86.350 98.1 102.1 102.7 103.3 102.1 101.575 117.3 126 127.2 130.4 127.2 125.490 136.5 146.1 147.5 154.0 149.3 148.8Mean 102.0 107.0 107.5 110.8 110.2 109.2

Changes in the distribution of leisure time, 1965 to 2003and 2003 to 2013

­50

510

15C

hang

e: H

ours

 per

 Wee

k

5 20 35 50 65 80 95Percentile of Distribution

Changes in market time by education category

1525

3545

55H

ours

1970 1980 1990 2000 2010Y ear

1525

3545

55H

ours

1970 1980 1990 2000 2010Y ear

I Market time decreases most for less educated men.

Changes in leisure time by education category

100

105

110

115

120

Hou

rs

1970 1980 1990 2000 2010Y ear

100

105

110

115

120

Hou

rs

1970 1980 1990 2000 2010Y ear

I Leisure time increases most for less educated men.

Changes in leisure time by education category

Change:’65-’13

WholeSample

< HighSchool

HighSchool

SomeCollege

> College

Eating −0.64 −1.43 −0.62 −0.85 0.18Sleeping 6.78 8.17 7.98 6.84 3.57Pers. Care −4.10 −4.42 −4.40 3.52 −3.82TV 8.70 9.66 9.74 8.35 6.46Non-TV Ent. 0.84 0.98 0.95 0.82 0.57Socializing −4.96 −3.89 −4.95 −4.77 −6.06Hobbies −0.91 −0.89 −1.05 −0.77 −0.79Reading −3.75 −3.38 −3.75 −3.55 −4.23Exercise 0.77 0.47 0.48 0.58 1.66Garden 1.19 1.17 1.34 1.12 1.04All Other 1.33 3.05 1.35 0.92 0.18

Conclusion

I Average leisure increases by approx. 5 hours.

I 90th percentile in leisure distribution increases from 137 to149 hours per week; 10th percentile is flat at 74-75 hours.

I Leisure increases are concentrated in high school graduates,dropouts.

I Is it possible to estimate the functions, φi , f from thebeginning of the presentation (where, again,

c = f(φ1 (x1, t1) , ..., φi (xi , ti ) , ..., φn (xn, tn)

))

How does inequality in∑xi compare to inequality in c?

Notes on Aguiar et al. (2013):"Time Use During theGreat Recession"

Introduction

I Main Question: How does leisure and home production timevary over the business cycle?

I Because of data limitations, this question has been (up tonow) diffi cult to answer.

I ATUS begins in 2003. Now have dataset spanning only onerecession.

I Challenge to separate trend from cycle, draw inference from 1recession.

I Strategy: Use geographic (cross-state) variation on changes inmarket hours. Many more observations.

Outline

I Data.I Aggregate results.I Cross-state results.I Implications for Benhabib et al (1991).

Data

I American Time Use Survey: 2003 to 2013 (2010 in the paper).I Similar categorization to Aguiar and Hurst (2007), with a fewextra categories

I Market work. Approx 32 hoursI Other income generating activities. 10 minutesI Job search. 15 minutes;I Nonmarket work. 18 hoursI Leisure: TV, Socializing, Sleeping, Eating & Personal Care.108 hours.

I Child care. 4.5 hours.I Other: Education, Religion activities, Own medical care. 5hours.

Leisure has increased by roughly 3 hours

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013106

107

108

109

110

111

112

AllMenWomen

I About half of the increase from sleeping, the other half fromTV watching.

Leisure roughly 25 minutes above trend in the GR

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013106.5

107

107.5

108

108.5

109

109.5

110

110.5

111

Homework 15 minutes above trend in the GR

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 201316.5

17

17.5

18

18.5

19

Market time 40 minutes above trend in the GR

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 201329.5

30

30.5

31

31.5

32

32.5

33

The method of de-trending matters

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 201329.5

30

30.5

31

31.5

32

32.5

33

The method of de-trending matters

Homework-Dev. from trend Leisure-Dev. from trend

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013­0.8

­0.6

­0.4

­0.2

0

0.2

0.4

0.6

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013­1.2

­1

­0.8

­0.6

­0.4

­0.2

0

0.2

0.4

0.6

I Deviation roughly 3× as large for homework, 40% higher forleisure, when using a quadratic trend.

I Not enough info from aggregate data ⇒ Use cross-statevariation.

Compare states with different market hours

∆τ jst = αj − βj∆τmarketst + εjst

s = state, t = period, j = activity

ALAL

AL

AL

AK

AKAK

AK

AZ

AZ

AZ

AZ

AR

ARAR

ARCACA CA

CA

COCO

CO COCTCT

CT

CT

DE

DE

DE

DEDC

DC

DC

DC

FL

FLFLFL

GA

GA

GA

GA HIHI

HI

HI

ID

ID

ID

ID

ILILIL

IL

IN

IN

IN

IN

IA

IA IA

IAKS

KSKS

KS

KY

KY

KY

KYLA

LALA

LAME

ME

ME

ME

MDMDMD

MD

MA

MA MA

MA

MI

MI

MI

MI

MNMN

MN

MN

MS

MSMS

MS

MO

MOMO

MO

MT

MTMT

MT

NE

NE

NENE

NVNV NV

NVNH

NHNH

NH

NJNJ

NJ NJ

NM

NM NM

NMNY

NY

NY

NY

NC

NC

NCNC

NDND

ND

ND

OH

OH OH

OH

OKOK OK

OK

OROR

ORORPA

PAPAPA

RI

RI

RI

RI

SCSCSC

SCSD

SDSD

SD

TNTN

TN

TNTX

TX

TX

TX

UT

UT

UT

UT

VT

VTVT

VT

VA

VA

VA

VAWA

WAWA

WA

WV

WV

WV

WV

WI WIWI

WIWY WY

WY

WY

AL

AK

AZ

ARCA CO

CT

DE

DC

FL GA

HI

ID

IL

IN

IA

KS

KYLAME MD

MA MIMNMS

MOMT

NE

NV

NHNJNM NYNC

ND

OH

OK

OR

PA

RI

SC

SD

TN

TX

UT

VT

VA

WAWV WI WY

­20

­10

010

20C

hang

e in

 Lei

sure

 Hou

rs

­20 ­10 0 10 20 30Change in work hours

ALAL

AL

AL

AZ

AZAZ

AZ

AR

ARAR

CACA CACA

COCO

CO CO CTCTCT

FL

FLFLFLGA

GA

GA

GA

ID

ID

ILILIL

IL

IN

IN

IN

IN

IA

IA IA

KSKSKS

KY

KY

KY

KYLA

LALA

LAMD

MDMDMD

MA

MA MA

MA

MI

MI

MI

MI

MNMN

MN

MNMS

MSMS

MO

MOMO

MO

NE

NE

NE

NVNV NVNJ NJNJ NJ

NM

NM NM

NY

NY

NY

NY

NC

NC

NCNCOH

OH OH

OH

OKOK OKOROR

ORORPA

PAPAPASC

SCSCSC

TNTN

TN

TNTX

TX

TX

TX

UT

UT

UTVA

VA

VA

VAWA

WAWA

WA

WV

WV

WV

WI WIWI

WI

AL

AZ

ARCA CO

CT

FL GAIL

IN

IA

KSKY

LAMDMA MIMNMS

MO

NENV

NJNM NYNC

OH

OK

ORPASCTN

TX

UT

VAWAWI

­20

­10

010

2030

Cha

nge 

in L

eisu

re H

ours

­20 ­10 0 10 20 30Change in work hours

β leisure ≈ 0.55

Compare states with different market hours

∆τ jst = αj − βj∆τmarketst + εjst

s = state, t = period, j = activity

AL

AL

ALAL

AK

AK AK

AK

AZ

AZAZ

AZAR

AR AR

ARCACA

CA

CACO

COCO

CO

CTCTCT

CTDE

DE

DEDE

DC

DC

DC

DCFLFL

FLFL

GA

GAGA

GA

HI

HI

HI

HI

ID

ID

ID

ID

ILIL

ILILIN

ININ

IN

IA

IAIA

IA

KS KSKS

KS

KY

KY

KYKYLA

LA

LA

LA

ME

ME

ME

ME MDMDMDMDMA

MA

MA

MAMI MI

MI

MI

MNMN

MNMN

MS

MSMSMSMO

MOMOMO

MT

MTMT

MT

NE

NE

NE

NE

NV

NV

NV

NV

NH

NHNH

NH

NJ

NJNJ

NJ

NM

NMNM

NM

NYNY NY

NY

NC

NCNC

NC

ND

ND

ND

ND

OHOH OHOH

OK

OK

OK

OKOR

OR

OR

OR

PA

PA PAPA

RI

RIRI

RI

SC

SCSC SC

SD

SD

SDSD

TN

TN TNTNTX

TX

TXTX

UT

UTUT

UT

VT

VT

VT

VT

VA

VAVA

VA

WA

WAWA

WAWV WVWV

WV

WI

WI

WI

WI

WY

WY

WY

WY

AL

AK

AZ

ARCA

CO

CT

DE

DCFLGA

HIID IL

IN

IA

KS

KY

LA

MEMD

MA

MI

MNMS

MO

MT

NE NV

NHNJ

NMNY

NC

ND

OHOKOR

PA

RI

SC

SD

TNTX

UT

VT

VAWA

WV

WI

WY

­10

010

20C

hang

e in

 Hom

e W

ork 

Hou

rs

­20 ­10 0 10 20 30Change in work hours

AL

AL

ALAL

AZ

AZAZ

AZAR

AR AR

CACA

CA

CACO

COCO

CO

CTCTCT FL

FLFL

FL

GA

GAGA

GA

ID

IDILIL

ILILIN

ININ

IN

IA

IAIA

KS KSKS

KY

KY

KYKYLA

LA

LA

LA

MDMDMDMD

MAMA

MA

MAMI MI

MI

MI

MNMN

MN

MN

MS

MSMS

MO

MOMOMO

NE

NE

NE NV

NV

NV

NJ

NJNJ

NJ

NM

NMNM

NYNY NY

NY

NC

NCNC

NC

OHOH OHOH

OK

OK

OK

OR

OR

OR

OR

PA

PAPA

PA

SC

SCSC SC

TN

TNTN

TNTX

TX

TXTX

UT

UTUT

VA

VAVA

VA

WA

WAWA

WAWV WV

WV

WI

WI

WI

WI

AL

AZ

ARCA

CO

CTFL

GAIL

IN

IA

KS

KY

LA

MD

MA

MI

MNMS

MO

NE NV

NJ

NMNY

NC

OHOKOR

PASC

TNTX

UT

VAWA

WI

­10

010

20C

hang

e in

 Hom

e W

ork 

Hou

rs­20 ­10 0 10 20 30

Change in work hours

βhome work ≈ 0.30

More Comparisons

SampleMean

β̂unweighted

β̂weighted

Other income-generating activities

0.14 7.9 0.9

Job search 0.23 2.8 1.5Child care 3.36 1.6 4.2Nonmarket work 13.03 29.1 31.8Core home production 6.91 13.2 13.3Home ownership activities 1.57 4.4 5.8

Leisure 79.52 55.6 52.7TV watching 13.07 12.4 13.2Socializing 5.61 8.5 7.2Sleeping 43.85 14.8 17.9Eating and personal care 9.75 0.5 -0.1Other leisure 7.23 19.5 14.4

Other 3.72 10.1 8.9

How to identify e from cross-state data?

I Simulate Benhabib, Rogerson, Wright model; 51 "states" and58 years of data (discard first 50 years).

I From the simulated data, regress

∆τhomest = αj − βj∆τmarketst + εjst

I Version 1 (2): Leisure includes (excludes) sleep.I Now try different values of e

VersionModele = 0.8

Modele = 0.5

DataFull Sample

DataRecession

1 0.74 0.46 0.50 0.572 0.48 0.20 0.39 0.47

How to identify "e" from cross-state data?

∆τhomest = αj − βj∆τmarketst + εjst

I σ ∈ [2.5, 4] ⇐⇒ e ∈ [0.6, 0.75]

Connections to structural transformation?

I Different groups of individuals (women, college+ educated)had faster labor income growth. Is this related to

I Increase in the prominence of services? (Problem Set 3)I Decline in the price of capital (particularly computer-relatedinvestment goods)?

I Time spent in home production declines (and women’s laborforce participation increases)

I ⇐declines in relative price of durable consumption goods?

I Capital share of income is increasing... Implications forinequality? (Problem Set 1)

Connections to other macro issues

I This paper: One example application of time use surveys:Reexamining changes in inequality

I One other example: Babcock and Marks (2010) examine timediaries of college students. Hours spent studying declines bya third between 1961 and 2003 ⇒ Declining production ofhuman capital.

I Data from other countries are also readily available:Multinational Time Use Survey (MTUS) is a harmonizeddataset of ˜ 20 (mainly developed) countries.

I Survey of Unemployed Workers in New Jersey: Individual-levelpanel of time use.